The role of transparency in recommender systems. Sinha, R. and Swearingen, K. CHI '02 extended abstracts on Human factors in computing systems - CHI '02, 2002.
The role of transparency in recommender systems [link]Website  abstract   bibtex   
Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous transparency (user understanding of why a Preliminary research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the users perspective. The goal of this research was to examine the role of particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. results indicate that users like and feel more confident about recommendations that they perceive as transparent.
@article{
 title = {The role of transparency in recommender systems},
 type = {article},
 year = {2002},
 identifiers = {[object Object]},
 keywords = {recommender systems,usability studies,www},
 pages = {830},
 websites = {http://portal.acm.org/citation.cfm?doid=506443.506619},
 id = {a4f8b8b7-f68c-3847-b8dd-0fd1a29fe241},
 created = {2018-03-17T11:57:07.567Z},
 file_attached = {false},
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 last_modified = {2018-12-14T12:16:31.962Z},
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 citation_key = {Sinha2002},
 notes = {Los usuarios que han participado en el estudio prefieren explicaciones en el proceso de recomendación, incluso aunque conozcan los productos que se les recomienda.},
 private_publication = {false},
 abstract = {Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous transparency (user understanding of why a Preliminary research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the users perspective. The goal of this research was to examine the role of particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. results indicate that users like and feel more confident about recommendations that they perceive as transparent.},
 bibtype = {article},
 author = {Sinha, Rashmi and Swearingen, Kirsten},
 journal = {CHI '02 extended abstracts on Human factors in computing systems  - CHI '02}
}
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